Bridging Regular and Irregular Time Series: A Unified Perspective

Most time-series forecasting models assume that all variables are observed on a shared grid with uniform temporal spacing. Real-world systems often violate this assumption: in irregular multivariate time series (IMTS), variables are observed asynchronously at non-uniform timestamps, with observation frequencies varying across variables. Canonical pre-alignment (CPA) maps these observations to a shared time–variable grid, thereby placing regular and irregular time series in a common representation. This representation raises a fundamental question: are forecasting models developed for regular time series truly incapable of handling irregular observations? To address this question, we establish a unified perspective on representative models developed for irregular time series by organizing them according to a common four-stage pipeline: normalization, input embedding, dependency modeling, and forecast generation. By combining this perspective with controlled experiments, we identify the key factors for effective irregular forecasting and develop BridgeTST from these findings. Across five irregular and five regular datasets, BridgeTST achieves strong forecasting performance in both settings. In contrast, representative irregular forecasting models generally perform poorly in the regular setting. Building on these findings, we further examine the boundary between regular and irregular forecasting.